Quality Through Coopetition: An Empiric Approach to Measure Population Outcomes for Emergency Care-Sensitive Conditions.

STUDY OBJECTIVE: We develop a novel approach for measuring regional outcomes for emergency care-sensitive conditions. METHODS: We used statewide inpatient hospital discharge data from the Pennsylvania Healthcare Cost Containment Council. This cross-sectional, retrospective, population-based analysis used International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes to identify admissions for emergency care-sensitive conditions (ischemic stroke, ST-segment elevation myocardial infarction, out-of-hospital cardiac arrest, severe sepsis, and trauma). We analyzed the origin and destination patterns of patients, grouped hospitals with a hierarchical cluster analysis, and defined boundary shapefiles for emergency care service regions. RESULTS: Optimal clustering configurations determined 10 emergency care service regions for Pennsylvania. CONCLUSION: We used cluster analysis to empirically identify regional use patterns for emergency conditions requiring a communitywide system response. This method of attribution allows regional performance to be benchmarked and could be used to develop population-based outcome measures after life-threatening illness and injury.

Developing Data to Support Effective Coordination of Nonprofit Hospital Community Benefit Investments.

EXECUTIVE SUMMARY: Nonprofit hospitals achieve tax exemption through community benefit investments. The objective of this study was to characterize urban and suburban nonprofit hospitals' community benefit expenditures and to estimate regional per capita community benefit spending relative to community need. Community benefit expenditures, both overall and by subtype, were compared for urban versus suburban nonprofit hospitals in a large metropolitan area, the greater Philadelphia region. Estimated zip code-level per capita expenditures were mapped in the urban core area. We found that urban hospitals report higher overall community benefit expenditures than suburban hospitals yet invest less in community health improvement services, both proportionally and absolutely, despite spending similar proportions on charity care. There is an overlap in hospital-identified community benefit service areas in the urban core, but the degree of overlap is not related to community poverty levels. There is significant variation in zip code-level per capita community benefit expenditures, which does not correlate with community need. Community benefit investments offer the potential to improve community health, yet without regional coordination, the ability to maximize the potential of these investments is limited. This study's findings highlight the need to implement policies that increase transparency, accountability, and regional coordination of community benefit spending.

A Content Analysis of Nonprofit Hospital Community Health Needs Assessments and Community Benefit Implementation Strategies in Philadelphia.

CONTEXT: Nonprofit hospitals are mandated to perform a community health needs assessment, develop an implementation strategy to address community needs, and invest in improving community health through community benefit investments in order to maintain the tax exemptions afforded nonprofit hospitals. OBJECTIVE: We sought to describe the regional health needs identified across community health needs assessments and the portfolio of implementation strategies reported to address those needs. DESIGN: The study provides a content analysis of community health needs assessments and implementation strategies for nonprofit hospitals in one urban region. SETTING: The study focused on nonprofit hospitals in Philadelphia, Pennsylvania. MAIN OUTCOME MEASURES: Community benefit documents were coded to characterize health needs and intervention activities using the 4 health factor categories of the County Health Rankings framework: clinical care, health behaviors, social and economic factors, and physical environment. RESULTS: Hospitals predominantly identified health needs related to access to care, especially mental health and dental care, and insurance coverage and costs of care. In many instances, there is little alignment between needs identified through the community health needs assessments and the reported implementation strategies. Specifically, dental care, behavioral health, substance abuse, social factors, and health care and prescription drug costs were all cited as important community needs but were infrequently targeted by implementation strategies. CONCLUSIONS: Nonprofit hospital community health needs assessments in Philadelphia predominantly identify needs related to access to care and to some extent health behaviors. There is incomplete alignment between the needs identified in hospital assessments and the needs targeted in implementation strategies, underscoring a need for regional coordination in community benefit investments. Improved regional coordination between hospitals serving the region may offer the opportunity to eliminate duplicative efforts and increase the amount of funds available to address unmet needs.

Reassessing the Stroke Belt: Using Small Area Spatial Statistics to Identify Clusters of High Stroke Mortality in the United States.

BACKGROUND AND PURPOSE: The stroke belt is described as an 8-state region with high stroke mortality across the southeastern United States. Using spatial statistics, we identified clusters of high stroke mortality (hot spots) and adjacent areas of low stroke mortality (cool spots) for US counties and evaluated for regional differences in county-level risk factors. METHODS: A cross-sectional study of stroke mortality was conducted using Multiple Cause of Death data (Centers for Disease Control and Prevention) to compute age-adjusted adult stroke mortality rates for US counties. Local indicators of spatial association statistics were used for hot-spot mapping. County-level variables were compared between hot and cool spots. RESULTS: Between 2008 and 2010, there were 393 121 stroke-related deaths. Median age-adjusted adult stroke mortality was 61.7 per 100 000 persons (interquartile range=51.4-74.7). We identified 705 hot-spot counties (22.4%) and 234 cool-spot counties (7.5%); 44.5% of hot-spot counties were located outside of the stroke belt. Hot spots had greater proportions of black residents, higher rates of unemployment, chronic disease, and healthcare utilization, and lower median income and educational attainment. CONCLUSIONS: Clusters of high stroke mortality exist beyond the 8-state stroke belt, and variation exists within the stroke belt. Reconsideration of the stroke belt definition and increased attention to local determinants of health underlying small area regional variability could inform targeted healthcare interventions.

Do hospital service areas and hospital referral regions define discrete health care populations?

BACKGROUND: Effective measurement of health care quality, access, and cost for populations requires an accountable geographic unit. Although Hospital Service Areas (HSAs) and Hospital Referral Regions (HRRs) have been extensively used in health services research, it is unknown whether these units accurately describe patterns of hospital use for patients living within them. OBJECTIVES: To evaluate the ability of HSAs, HRRs, and counties to define discrete health care populations. RESEARCH DESIGN: Cross-sectional geographic analysis of hospital admissions. SUBJECTS: All hospital admissions during the year 2011 in Washington, Arizona, and Florida. MEASURES: The main outcomes of interest were 3 metrics that describe patient movement across HSA, HRR, and county boundaries: localization index, market share index, and net patient flow. Regression models tested the association of these metrics with different HSA characteristics. RESULTS: For 45% of HSAs, fewer than half of the patients were admitted to hospitals located in their HSA of residence. For 16% of HSAs, more than half of the treated patients lived elsewhere. There was an equivalent degree of movement across county boundaries but less movement across HRR boundaries. Patients living in populous, urban HSAs with multiple, large, and teaching hospitals tended to remain for inpatient care. Patients admitted through the emergency department tended to receive care at local hospitals relative to other patients. CONCLUSIONS: HSAs and HRRs are geographic units commonly used in health services research yet vary in their ability to describe where patients receive hospital care. Geographic models may need to account for differences between emergent and nonemergent care.

Geography, Not Health System Affiliations, Determines Patients' Revisits to the Emergency Department.

OBJECTIVES: To determine how frequently patients revisit the emergency department after an initial encounter, and to describe revisit capture rates for the same hospital, health system, and geographic region. DATA SOURCES/STUDY SETTING: Florida state data from January 1, 2010, to June 30, 2011, from the Healthcare Cost and Utilization Project. STUDY DESIGN: This is a retrospective cohort study of emergency department return visits among Florida adults over an 18-month period. We evaluated pairs of index and 30-day return emergency department visits and compared capture rates for hospital, health system, and geographic units. DATA COLLECTION/EXTRACTION METHODS: Data were obtained from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project and the American Hospital Association Annual Survey Database. PRINCIPAL FINDINGS: Among 9,416,212 emergency department visits, 22.6 percent (2,124,441) were associated with a 30-day return. Seventy percent (1,477,772) of 30-day returns occurred to the same hospital. The 30-day return capture rates were highest within the same geographic area: county-level capture at 92 percent (IQR=86-96 percent) versus health system capture at 75 percent (IQR = 68-81 percent). CONCLUSIONS: Acute care utilization patterns are often independent of health system boundaries. Current population-based health care models that attribute patients to a single provider or health system may be strengthened by considering geographic patterns of acute care utilization.

Measuring Emergency Care Survival: The Implications of Risk Adjusting for Race and Poverty.

OBJECTIVES: We determined the impact of including race, ethnicity, and poverty in risk adjustment models for emergency care-sensitive conditions mortality that could be used for hospital pay-for-performance initiatives. We hypothesized that adjusting for race, ethnicity, and poverty would bolster rankings for hospitals that cared for a disproportionate share of nonwhite, Hispanic, or poor patients. METHODS: We performed a cross-sectional analysis of patients admitted from the emergency department to 157 hospitals in Pennsylvania with trauma, sepsis, stroke, cardiac arrest, and ST-elevation myocardial infarction. We used multivariable logistic regression models to predict in-hospital mortality. We determined the predictive accuracy of adding patient race and ethnicity (dichotomized as non-Hispanic white vs. all other Hispanic or nonwhite patients) and poverty (uninsured, on Medicaid, or lowest income quartile zip code vs. all others) to other patient-level covariates. We then ranked each hospital on observed-to-expected mortality, with and without race, ethnicity, and poverty in the model, and examined characteristics of hospitals with large changes between models. RESULTS: The overall mortality rate among 170,750 inpatients was 6.9%. Mortality was significantly higher for nonwhite and Hispanic patients (adjusted odds ratio [aOR] = 1.27, 95% confidence interval [CI] = 1.19-1.36) and poor patients (aOR = 1.21, 95% CI = 1.12-1.31). Adding race, ethnicity, and poverty to the risk adjustment model resulted in a small increase in C-statistic (0.8260 to 0.8265, p = 0.002). No hospitals moved into or out of the highest-performing decile when adjustment for race, ethnicity, and poverty was added, but the three hospitals that moved out of the lowest-performing decile, relative to other hospitals, had significantly more nonwhite and Hispanic patients (68% vs. 11%, p < 0.001) and poor patients (56% vs. 10%, p < 0.001). CONCLUSIONS: Sociodemographic risk adjustment of emergency care-sensitive mortality improves apparent performance of some hospitals treating a large number of nonwhite, Hispanic, or poor patients. This may help these hospitals avoid financial penalties in pay-for-performance programs.